activity
20142022
most citedDeep Unfolding: Model-Based Inspiration of Novel Deep Architectures

255 citations · 279 across the 9 of their papers we have counts for

collaborators

9 papers

eess.AS2022★ 1 cited

Conformer with dual-mode chunked attention for joint online and offline ASR

Felix Weninger, Marco Gaudesi, Md Akmal Haidar +3

In this paper, we present an in-depth study on online attention mechanisms and distillation techniques for dual-mode (i.e., joint online and offline) ASR using the Conformer Transd…

eess.AS2021

ChannelAugment: Improving generalization of multi-channel ASR by training with input channel randomization

Marco Gaudesi, Felix Weninger, Dushyant Sharma +1

End-to-end (E2E) multi-channel ASR systems show state-of-the-art performance in far-field ASR tasks by joint training of a multi-channel front-end along with the ASR model. The mai…

eess.AS2021

Dual-Encoder Architecture with Encoder Selection for Joint Close-Talk and Far-Talk Speech Recognition

Felix Weninger, Marco Gaudesi, Ralf Leibold +2

In this paper, we propose a dual-encoder ASR architecture for joint modeling of close-talk (CT) and far-talk (FT) speech, in order to combine the advantages of CT and FT devices fo…

eess.AS2020★ 12 cited

Dyadic Speech-based Affect Recognition using DAMI-P2C Parent-child Multimodal Interaction Dataset

Huili Chen, Yue Zhang, Felix Weninger +3

Automatic speech-based affect recognition of individuals in dyadic conversation is a challenging task, in part because of its heavy reliance on manual pre-processing. Traditional a…

cs.LG2020

openXDATA: A Tool for Multi-Target Data Generation and Missing Label Completion

Felix Weninger, Yue Zhang, Rosalind W. Picard

A common problem in machine learning is to deal with datasets with disjoint label spaces and missing labels. In this work, we introduce the openXDATA tool that completes the missin…

eess.AS2020★ 3 cited

Semi-Supervised Learning with Data Augmentation for End-to-End ASR

Felix Weninger, Franco Mana, Roberto Gemello +2

In this paper, we apply Semi-Supervised Learning (SSL) along with Data Augmentation (DA) for improving the accuracy of End-to-End ASR. We focus on the consistency regularization pr…

eess.AS2019★ 2 cited

Listen, Attend, Spell and Adapt: Speaker Adapted Sequence-to-Sequence ASR

Felix Weninger, Jesús Andrés-Ferrer, Xinwei Li +1

Sequence-to-sequence (seq2seq) based ASR systems have shown state-of-the-art performances while having clear advantages in terms of simplicity. However, comparisons are mostly done…

cs.CL2014★ 6 cited

A Broadcast News Corpus for Evaluation and Tuning of German LVCSR Systems

Felix Weninger, Björn Schuller, Florian Eyben +2

Transcription of broadcast news is an interesting and challenging application for large-vocabulary continuous speech recognition (LVCSR). We present in detail the structure of a ma…

cs.LG2014★ 255 cited

Deep Unfolding: Model-Based Inspiration of Novel Deep Architectures

John R. Hershey, Jonathan Le Roux, Felix Weninger

Model-based methods and deep neural networks have both been tremendously successful paradigms in machine learning. In model-based methods, problem domain knowledge can be built int…